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Record W4407761729 · doi:10.1063/5.0256633

Optimization of an empirical equation in drop shape theory and a structural–mechanical analysis of a caved ore and rock particle flow system

2025· article· en· W4407761729 on OpenAlexaff
Hao Sun, Lishan Zhao, Davide Elmo, Lichang Wei, Shenggui Zhou

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsVoid (composites)Void ratioMechanicsIsotropyEllipsoidDrop (telecommunication)Shape factorGeotechnical engineeringPhysicsMaterials scienceComposite materialGeometryGeologyEngineeringMathematicsMechanical engineeringOptics

Abstract

fetched live from OpenAlex

The void ratio of the caved ore and rock particle system, which is a common and critical factor affecting draw, has not received much attention. In this paper, numerical draw tests were conducted to investigate the flow mechanism of caved ore and rock under varying void ratios. The study established a quantitative relationship between the void ratio of the particle system and the shape evolution of the isolated extraction zone (IEZ). To optimize the empirical equation for the IEZ shape, the drop shape (DS) theory was employed. Furthermore, a structural-mechanical analysis of the packing and flow particle systems was performed using the statistical mechanics method. The results demonstrated the following findings: (1) The void ratio significantly impacts the structural-mechanical characteristics of the gravity flow systems. As the void ratio increases, the IEZ shape transitions from a DS to an approximately ellipsoid shape and eventually to an upside-down DS. (2) With an increasing void ratio, the packing system of the caved ore and rock tends to become isotropic. This leads to a decrease in the degree of order, average coordination number, and average stress. (3) Notable correlations and consistency are observed in the laws governing the evolution of stress.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.267
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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